Provides a good overview, but I felt some loose ends were not addressed in terms of how stringent the conditions need to be met, and if one can use MLR when this is not the case.

Other nits about the depth and breadth of the course aside, I thought it was a good course. The main critique I have to offer is the lack of emphasis of using the power of R. When teaching model selection, the course should have at least provided instruction—or at least a written resource—on how to write the R code for automating forward/backward selection by R^2.* Being a course about using R as well as about linear regression and modeling, it seems like the appropriate thing to do.

(*A classmate whose final project I peer-reviewed used for loops to run the forward model selection based on R^2. That's how I learned about it.)

Covers the basic of Linear Regression, would like to see more advanced material.

The mathematical depth of this course, is insufficient even at its targeted level, and therefore a lot of practical manipulations of the data, and fine tuning of the model could be had if a week more has been put into this course.

Easy does not equate fun, after completing this course, I left the specialization.

Compared to other courses in the specification, this course content is too shallow and brief.

The grading is just so annoying

lectures are great.

but no tutor answers my question posted in discussion board.

not so good